Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium

Bridget M. Lin, Kelsey E. Grinde, Jennifer A. Brody, Charles E. Breeze, Laura M. Raffield, Josyf C. Mychaleckyj, Timothy A. Thornton, James A. Perry, Leslie J. Baier, Lisa de las Fuentes, Xiuqing Guo, Benjamin D. Heavner, Robert L. Hanson, Yi Jen Hung, Huijun Qian, Chao A. Hsiung, Shih Jen Hwang, Margaret R. Irvin, Deepti Jain, Tanika N. KellySayuko Kobes, Leslie Lange, James P. Lash, Yun Li, Xiaoming Liu, Xuenan Mi, Solomon K. Musani, George J. Papanicolaou, Afshin Parsa, Alex P. Reiner, Shabnam Salimi, Wayne H.H. Sheu, Alan R. Shuldiner, Kent D. Taylor, Albert V. Smith, Jennifer A. Smith, Adrienne Tin, Dhananjay Vaidya, Robert B. Wallace, Kenichi Yamamoto, Saori Sakaue, Koichi Matsuda, Yoichiro Kamatani, Yukihide Momozawa, Lisa R. Yanek, Betsi A. Young, Wei Zhao, Yukinori Okada, Gonzalo Abecasis, Bruce M. Psaty, Donna K. Arnett, Eric Boerwinkle, Jianwen Cai, Ida Yii-Der Chen, Adolfo Correa, L. Adrienne Cupples, Jiang He, Sharon LR Kardia, Charles Kooperberg, Rasika A. Mathias, Braxton D. Mitchell, Deborah A. Nickerson, Steve T. Turner, Vasan S. Ramachandran, Jerome I. Rotter, Daniel Levy, Holly J. Kramer, Anna Köttgen, Trans-Omics for Precision Medicine (TOPMed) Consortium NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Kidney Working Group TOPMed Kidney Working Group, Stephen S. Rich, Dan Yu Lin, Sharon R. Browning, Nora Franceschini

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Background: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. Methods: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. Findings: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10−11; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10−9; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10−9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10−9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10−9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. Interpretation: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.

Original languageEnglish (US)
Article number103157
StatePublished - Jan 2021
Externally publishedYes


  • Ancestry-specific variants
  • Kidney traits
  • Rare variants
  • Whole genome sequencing

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology


Dive into the research topics of 'Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium'. Together they form a unique fingerprint.

Cite this